FIRE

FIRE identifies and characterizes DNA and RNA regulatory motifs by quantifying mutual information between sequence features and gene expression measurements.

Key Features:

• Mutual Information Framework: Employs mutual information to assess the dependency between sequence data and gene expression measurements for motif detection.
• Minimal Assumptions: Operates with few presuppositions about background sequence models or regulatory mechanisms, enabling application across diverse genomes and data types.
• High Sensitivity and Specificity: Delivers high sensitivity with reported near-zero false-positive rates in motif detection.

Scientific Applications:

• Transcription Factor and miRNA Target Identification: Detects putative and established transcription factor binding sites and microRNA (miRNA) target sites associated with gene expression changes.
• Spatial Configuration Analysis: Reveals diversity in the spatial configurations of DNA and RNA motifs relevant to regulatory function.
• Co-occurrence Analysis: Identifies pervasive co-occurrence patterns between DNA and RNA motifs to investigate potential regulatory interactions.
• Context-Dependent Motif Avoidance: Detects selection for motif avoidance dependent on sequence context.
• Posttranscriptional Impact on Transcriptomes: Analyzes posttranscriptional processes to assess their impact on eukaryotic transcriptomes.

Methodology:

Computes mutual information directly between sequence features and gene expression measurements to detect regulatory motifs without relying on predefined background sequence models or regulatory assumptions.